Path planning and navigation have been studied for many years in connection with a variety of fields of study such as, for example, in the fields of Robotics and Computational Geometry. In these or similar contexts, environmental conditions are presumed to be globally available and such information is further presumed to be universally shared with all other elements. Thus, centralized computing can be conducted with nearly perfect global knowledge of the environment.
However, navigation through a wireless sensor network presents a number of difficulties. For example, in such cases, the navigation typically must be performed in a distributed manner over a self-organized network consisting of a huge number of nodes. Thus, environmental information typically must be rapidly captured with respect to the distributed nodes. In addition, such navigation architecture would, in many cases, be required to rapidly adapt and react to environmental variations.
Previous research has been directed toward problems relating to solving the minimum or maximum exposure path in a network. In particular, conventional literature largely relies on exhaustive search over the entire network, which can be too slow for many practical applications. In addition, conventional systems have also proposed the use of heuristics to compute paths in a distributed manner. However, these solutions treat individual sensor nodes as adversaries rather than utilizing them as infrastructures and thus do not provide an adequate solution for navigation in dynamic environments.